A Comparative Study of Metaheuristics for Liver Disorders Prediction
Authors: 
F. Bekaddour, S. ChikhiAuthors Info & Claims
Pages 1 - 6
Abstract
Nowadays, metaheuristics are widely used for solving complex real world problem. Liver disorders prediction, investigates many researchers to develop system based metaheuristics to help physicians in their diagnosis task. In this article, we propose the use of four metaheuristics: Particle swarm optimization, Simulated Annealing, Genetic Algorithm and Homogeneity Based-Algorithm, to identify the liver disorders of a patient and compare the best performance among them. Additionally, we use the Improved Homogeneity-Based Algorithm (IHBA) metaheuristic and we adopt the objective function formula proposed in our work as criteria of performance evaluation based on the BUPA Liver Disorders dataset. The computational experiments confirmed that the Improved Homogeneity Based metaheuristic outperforms the other approaches.
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Information
Published In
November 2016
163 pages
ISBN:9781450348768
DOI:10.1145/3038884
- General Chairs:
- Chawki Djeddi,
- Imran Siddiqi,
- Akram Bennour,
- Program Chairs:
- Youcef Chibani,
- Haikal El Abed
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Association for Computing Machinery
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Published: 22 November 2016
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MedPRAI-2016
MedPRAI-2016: Mediterranean Conference on Pattern Recognition and Artificial Intelligence
November 22 - 23, 2016
Tebessa, Algeria
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